EksplorasiData_Praveen Arvind Krisna.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#Nama : Praveen Arvind Krisna\n",
    "#NIM : 13219022\n",
    "#Asal Universitas : Institut Teknologi Bandung\n",
    "#Tanggal pengerjaan tugas : 20/11/2020\n",
    "#Sumber Dataset = TakeMeOut\n",
    "#Alur data : Data Observation - Data Cleaning - Data Splitting - Model Development\n",
    "import pandas as pd\n",
    "import array as ar\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('takemeout.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = {'Siapa nama kamu?':'nama', 'Cewek atau cowok nih?':'gender', 'Seberapa penting quality time bareng calon pacar untuk kamu?':'time','Seberapa penting physical touch sama calon pacar untuk kamu?':'touch','Seberapa penting word of affirmation dari calon pacar untuk kamu?':'word', 'Seberapa penting dapet kado dari calon pacar untuk kamu?':'kado','Seberapa penting bantuan dari calon pacar untuk kamu?':'help'  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Timestamp', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m = df[df['gender'] == \"Cowok\"]\n",
    "m_val = df_m.drop(['nama', 'gender'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f = df[df['gender'] == \"Cewek\"]\n",
    "f_val = df_f.drop(['nama', 'gender'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic Statistic group by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rata rata tiap gender untuk tiap kolom\n",
    "df.groupby([\"gender\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#value minimal tiap gender untuk tiap kolom\n",
    "df.groupby([\"gender\"]).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#value maksimal tiap gender untuk tiap kolom\n",
    "df.groupby([\"gender\"]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nilai tengah untuk tiap gender di tiap kolom\n",
    "df.groupby([\"gender\"]).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nilai terbanyak untuk tiap kolom dari gender sampai help\n",
    "dropNama = df.drop(['nama'], axis=1)\n",
    "stats.mode(dropNama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jumlah cowok\n",
    "len(m_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jumlah cewek\n",
    "len(f_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_coup = NearestNeighbors(n_neighbors=5).fit(m_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_coup = NearestNeighbors(n_neighbors=5).fit(f_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['nama', 'gender'], axis=1)\n",
    "df_new = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nama = df_new['nama'] == 'P********************'\n",
    "a = x[nama].values\n",
    "p = 0\n",
    "for i in range(len(df)):\n",
    "    if(nama[i] == True):\n",
    "        p = df.at[i, 'gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hasil = 0\n",
    "if(p == \"Cowok\"):\n",
    "    __, hasil = f_coup.kneighbors(a)\n",
    "else:\n",
    "    __, hasil = m_coup.kneighbors(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Analisis untuk 5 pasangan terdekat dengan total values yang sama\n",
    "df.iloc[hasil[0]]['nama']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analisis Untuk pasangan tercocok yang memiliki values yang sama persis\n",
    "df_def = 0\n",
    "df_comp = 0\n",
    "if(p == \"Cewek\"):\n",
    "    df_def = df_m.drop(['nama', 'gender'], axis=1)\n",
    "    df_comp = df_m\n",
    "elif(p == \"Cowok\"):\n",
    "    df_def = df_f.drop(['nama', 'gender'], axis=1)\n",
    "    df_comp = df_f\n",
    "jumlah = 0\n",
    "for i in range(len(df_def)):\n",
    "    if(np.all(a == df_def[i:i+1].values)):\n",
    "        print(df_comp[i:i+1]['nama'])\n",
    "    else:\n",
    "        jumlah += 1\n",
    "if(jumlah == len(df_def)):\n",
    "    print(\"Tidak ada pasangan yang cocok sekali\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analisis Untuk Pasangan dengan range maksimal 1 poin untuk masing masing values\n",
    "jumlah = 0\n",
    "for i in range(len(df_def)):\n",
    "    data = df_def[i:i+1].values\n",
    "    counter = 0\n",
    "    for j in range(5):\n",
    "        if(data[0, j] <= a[0, j]+1):\n",
    "            if(data[0, j] >= a[0, j]-1):\n",
    "                counter += 1\n",
    "    if(counter == 5):\n",
    "        print(df_comp[i:i+1]['nama'])\n",
    "        counter = 0\n",
    "    else:        \n",
    "        counter = 0\n",
    "        jumlah += 1\n",
    "if(jumlah == len(df_def)):\n",
    "    print(\"Tidak ada pasangan yang cocok sekali\")"
   ]
  }
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